





import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation






def lowerCamelCase__       (				snake_case_      :    List[Any]			) ->							Union[str, Any]:
   __snake_case							=  384
   __snake_case							=  7
   if "tiny" in model_name:
      __snake_case							=  96
      __snake_case							=  (2, 2, 6, 2)
      __snake_case							=  (3, 6, 12, 24)
   elif "small" in model_name:
      __snake_case							=  96
      __snake_case							=  (2, 2, 18, 2)
      __snake_case							=  (3, 6, 12, 24)
   elif "base" in model_name:
      __snake_case							=  128
      __snake_case							=  (2, 2, 18, 2)
      __snake_case							=  (4, 8, 16, 32)
      __snake_case							=  12
      __snake_case							=  512
   elif "large" in model_name:
      __snake_case							=  192
      __snake_case							=  (2, 2, 18, 2)
      __snake_case							=  (6, 12, 24, 48)
      __snake_case							=  12
      __snake_case							=  768

   # set label information
   __snake_case							=  150
   __snake_case							=  '''huggingface/label-files'''
   __snake_case							=  '''ade20k-id2label.json'''
   __snake_case							=  json.load(open(hf_hub_download(a__			, a__			, repo_type='''dataset'''			)			, '''r'''			)			)
   __snake_case							=  {int(a__			): v for k, v in idalabel.items()}
   __snake_case							=  {v: k for k, v in idalabel.items()}

   __snake_case							=  SwinConfig(
       embed_dim=a__			, depths=a__			, num_heads=a__			, window_size=a__			, out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']			, )
   __snake_case							=  UperNetConfig(
       backbone_config=a__			, auxiliary_in_channels=a__			, num_labels=a__			, idalabel=a__			, labelaid=a__			, )

   return config






def lowerCamelCase__       (				snake_case_      :    Union[str, Any]			) ->							str:
   __snake_case							=  []

   # fmt: off
   # stem
   rename_keys.append(('''backbone.patch_embed.projection.weight''', '''backbone.embeddings.patch_embeddings.projection.weight''')			)
   rename_keys.append(('''backbone.patch_embed.projection.bias''', '''backbone.embeddings.patch_embeddings.projection.bias''')			)
   rename_keys.append(('''backbone.patch_embed.norm.weight''', '''backbone.embeddings.norm.weight''')			)
   rename_keys.append(('''backbone.patch_embed.norm.bias''', '''backbone.embeddings.norm.bias''')			)
   # stages
   for i in range(len(config.backbone_config.depths			)			):
      for j in range(config.backbone_config.depths[i]			):
         rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.norm1.weight""", f"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.weight""")			)
         rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.norm1.bias""", f"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.bias""")			)
         rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_bias_table""", f"""backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table""")			)
         rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_index""", f"""backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index""")			)
         rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.weight""", f"""backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight""")			)
         rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.bias""", f"""backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias""")			)
         rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.norm2.weight""", f"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.weight""")			)
         rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.norm2.bias""", f"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.bias""")			)
         rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.weight""", f"""backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight""")			)
         rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.bias""", f"""backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias""")			)
         rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.ffn.layers.1.weight""", f"""backbone.encoder.layers.{i}.blocks.{j}.output.dense.weight""")			)
         rename_keys.append((f"""backbone.stages.{i}.blocks.{j}.ffn.layers.1.bias""", f"""backbone.encoder.layers.{i}.blocks.{j}.output.dense.bias""")			)

      if i < 3:
         rename_keys.append((f"""backbone.stages.{i}.downsample.reduction.weight""", f"""backbone.encoder.layers.{i}.downsample.reduction.weight""")			)
         rename_keys.append((f"""backbone.stages.{i}.downsample.norm.weight""", f"""backbone.encoder.layers.{i}.downsample.norm.weight""")			)
         rename_keys.append((f"""backbone.stages.{i}.downsample.norm.bias""", f"""backbone.encoder.layers.{i}.downsample.norm.bias""")			)
      rename_keys.append((f"""backbone.norm{i}.weight""", f"""backbone.hidden_states_norms.stage{i+1}.weight""")			)
      rename_keys.append((f"""backbone.norm{i}.bias""", f"""backbone.hidden_states_norms.stage{i+1}.bias""")			)

   # decode head
   rename_keys.extend(
       [
           ('''decode_head.conv_seg.weight''', '''decode_head.classifier.weight'''),
           ('''decode_head.conv_seg.bias''', '''decode_head.classifier.bias'''),
           ('''auxiliary_head.conv_seg.weight''', '''auxiliary_head.classifier.weight'''),
           ('''auxiliary_head.conv_seg.bias''', '''auxiliary_head.classifier.bias'''),
       ]			)
   # fmt: on

   return rename_keys






def lowerCamelCase__       (				snake_case_      :    str			, snake_case_      :    Union[str, Any]			, snake_case_      :    Dict			) ->							int:
   __snake_case							=  dct.pop(a__			)
   __snake_case							=  val






def lowerCamelCase__       (				snake_case_      :    Tuple			, snake_case_      :    Optional[int]			) ->							Dict:
   __snake_case							=  [int(backbone_config.embed_dim * 2**i			) for i in range(len(backbone_config.depths			)			)]
   for i in range(len(backbone_config.depths			)			):
      __snake_case							=  num_features[i]
      for j in range(backbone_config.depths[i]			):
         # fmt: off
         # read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
         __snake_case							=  state_dict.pop(f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.weight"""			)
         __snake_case							=  state_dict.pop(f"""backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.bias"""			)
         # next, add query, keys and values (in that order) to the state dict
         __snake_case							=  in_proj_weight[:dim, :]
         __snake_case							=  in_proj_bias[: dim]
         __snake_case							=  in_proj_weight[
             dim : dim * 2, :
         ]
         __snake_case							=  in_proj_bias[
             dim : dim * 2
         ]
         __snake_case							=  in_proj_weight[
             -dim :, :
         ]
         __snake_case							=  in_proj_bias[-dim :]
         # fmt: on






def lowerCamelCase__       (				snake_case_      :    str			) ->							Optional[Any]:
   __snake_case			,      __snake_case							=  x.shape
   __snake_case							=  x.reshape(a__			, 4			, in_channel // 4			)
   __snake_case							=  x[:, [0, 2, 1, 3], :].transpose(1			, 2			).reshape(a__			, a__			)
   return x






def lowerCamelCase__       (				snake_case_      :    str			) ->							Union[str, Any]:
   __snake_case			,      __snake_case							=  x.shape
   __snake_case							=  x.reshape(a__			, in_channel // 4			, 4			)
   __snake_case							=  x[:, :, [0, 2, 1, 3]].transpose(1			, 2			).reshape(a__			, a__			)

   return x






def lowerCamelCase__       (				snake_case_      :    List[Any]			) ->							Optional[int]:
   __snake_case							=  x.shape[0]
   __snake_case							=  x.reshape(4			, in_channel // 4			)
   __snake_case							=  x[[0, 2, 1, 3], :].transpose(0			, 1			).reshape(a__			)
   return x






def lowerCamelCase__       (				snake_case_      :    Optional[int]			) ->							Optional[Any]:
   __snake_case							=  x.shape[0]
   __snake_case							=  x.reshape(in_channel // 4			, 4			)
   __snake_case							=  x[:, [0, 2, 1, 3]].transpose(0			, 1			).reshape(a__			)
   return x






def lowerCamelCase__       (				snake_case_      :    Optional[int]			, snake_case_      :    Optional[int]			, snake_case_      :    str			) ->							List[Any]:
   __snake_case							=  {
       '''upernet-swin-tiny''': '''https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210531_112542-e380ad3e.pth''',
       '''upernet-swin-small''': '''https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192015-ee2fff1c.pth''',
       '''upernet-swin-base''': '''https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K_20210531_125459-429057bf.pth''',
       '''upernet-swin-large''': '''https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k_20220318_091743-9ba68901.pth''',
   }
   __snake_case							=  model_name_to_url[model_name]
   __snake_case							=  torch.hub.load_state_dict_from_url(a__			, map_location='''cpu'''			, file_name=a__			)[
       '''state_dict'''
   ]

   for name, param in state_dict.items():
      print(a__			, param.shape			)

   __snake_case							=  get_upernet_config(a__			)
   __snake_case							=  UperNetForSemanticSegmentation(a__			)
   model.eval()

   # replace "bn" => "batch_norm"
   for key in state_dict.copy().keys():
      __snake_case							=  state_dict.pop(a__			)
      if "bn" in key:
         __snake_case							=  key.replace('''bn'''			, '''batch_norm'''			)
      __snake_case							=  val

   # rename keys
   __snake_case							=  create_rename_keys(a__			)
   for src, dest in rename_keys:
      rename_key(a__			, a__			, a__			)
   read_in_q_k_v(a__			, config.backbone_config			)

   # fix downsample parameters
   for key, value in state_dict.items():
      if "downsample" in key:
         if "reduction" in key:
            __snake_case							=  reverse_correct_unfold_reduction_order(a__			)
         if "norm" in key:
            __snake_case							=  reverse_correct_unfold_norm_order(a__			)

   model.load_state_dict(a__			)

   # verify on image
   __snake_case							=  '''https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg'''
   __snake_case							=  Image.open(requests.get(a__			, stream=a__			).raw			).convert('''RGB'''			)

   __snake_case							=  SegformerImageProcessor()
   __snake_case							=  processor(a__			, return_tensors='''pt'''			).pixel_values

   with torch.no_grad():
      __snake_case							=  model(a__			)
      __snake_case							=  outputs.logits

   print(logits.shape			)
   print('''First values of logits:'''			, logits[0, 0, :3, :3]			)
   # assert values
   if model_name == "upernet-swin-tiny":
      __snake_case							=  torch.tensor(
          [[-7.5_958, -7.5_958, -7.4_302], [-7.5_958, -7.5_958, -7.4_302], [-7.4_797, -7.4_797, -7.3_068]]			)
   elif model_name == "upernet-swin-small":
      __snake_case							=  torch.tensor(
          [[-7.1_921, -7.1_921, -6.9_532], [-7.1_921, -7.1_921, -6.9_532], [-7.0_908, -7.0_908, -6.8_534]]			)
   elif model_name == "upernet-swin-base":
      __snake_case							=  torch.tensor(
          [[-6.5_851, -6.5_851, -6.4_330], [-6.5_851, -6.5_851, -6.4_330], [-6.4_763, -6.4_763, -6.3_254]]			)
   elif model_name == "upernet-swin-large":
      __snake_case							=  torch.tensor(
          [[-7.5_297, -7.5_297, -7.3_802], [-7.5_297, -7.5_297, -7.3_802], [-7.4_044, -7.4_044, -7.2_586]]			)
   print('''Logits:'''			, outputs.logits[0, 0, :3, :3]			)
   assert torch.allclose(outputs.logits[0, 0, :3, :3]			, a__			, atol=1e-4			)
   print('''Looks ok!'''			)

   if pytorch_dump_folder_path is not None:
      print(f"""Saving model {model_name} to {pytorch_dump_folder_path}"""			)
      model.save_pretrained(a__			)
      print(f"""Saving processor to {pytorch_dump_folder_path}"""			)
      processor.save_pretrained(a__			)

   if push_to_hub:
      print(f"""Pushing model and processor for {model_name} to hub"""			)
      model.push_to_hub(f"""openmmlab/{model_name}"""			)
      processor.push_to_hub(f"""openmmlab/{model_name}"""			)


if __name__ == "__main__":
    snake_case_           =     argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '--model_name',
        default='upernet-swin-tiny',
        type=str,
        choices=[F'upernet-swin-{size}' for size in ['tiny', 'small', 'base', 'large']],
        help='Name of the Swin + UperNet model you\'d like to convert.',
    )
    parser.add_argument(
        '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
    )
    parser.add_argument(
        '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
    )

    snake_case_           =     parser.parse_args()
    convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)


def 				lowerCAmelCase__       (			a__    ,     a__    ,     a__    ,     a__    ,     a__ )      ->int:




  '''simple docstring'''

  if index == number_of_items:
    return 0
  _UpperCamelCase           =    0
  _UpperCamelCase           =    0
  _UpperCamelCase           =    knapsack(a__    ,     a__    ,     a__    ,     a__    ,     index + 1 )
  if weights[index] <= max_weight:
    _UpperCamelCase           =    values[index] + knapsack(
        a__    ,     a__    ,     a__    ,     max_weight - weights[index]    ,     index + 1 )
  return max(a__    ,     a__ )


if __name__ == "__main__":
   import doctest

   doctest.testmod()




